Remove TF-TRT examples and point users to the new examples repo (#7330)

* Fix TensorRT test output and add int8 result.

* Fix comments

* Removing footnote declaration as well

* Remove TF-TRT examples and point users to new example repo

* Update urls

* Fix review comments and links

research/tensorrt/README.md

-# Running the TensorFlow Official ResNet with TensorRT
+# Tensorflow-TensorRT integration
 
-[TensorRT](https://developer.nvidia.com/tensorrt) is NVIDIA's inference
-optimizer for deep learning. Briefly, TensorRT rewrites parts of the
-execution graph to allow for faster prediction times.
+Please refer to the
+[API for TF 1.x](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/compiler/tensorrt/trt_convert.py#L295-L325)
+and
+[API for TF 2.0](https://github.com/tensorflow/tensorflow/blob/r2.0/tensorflow/python/compiler/tensorrt/trt_convert.py#L766-L859)
+for how to use TF-TRT.
 
-Here we provide a sample script that can:
+Also see:
 
-1. Convert a TensorFlow SavedModel to a Frozen Graph.
-2. Load a Frozen Graph for inference.
-3. Time inference loops using the native TensorFlow graph.
-4. Time inference loops using FP32, FP16, or INT8 precision modes from TensorRT.
-
-We provide some results below, as well as instructions for running this script.
-
-## How to Run This Script
-
-### Step 1: Install Prerequisites
-
-1. [Install TensorFlow.](https://www.tensorflow.org/install/)
-2. [Install TensorRT.](http://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html)
-3. We use the image processing functions from the [Official version of ResNet](/official/resnet/imagenet_preprocessing.py). Please checkout the Models repository if you haven't
-already, and add the Official Models to your Python path:
-
-```
-git clone https://github.com/tensorflow/models.git
-export PYTHONPATH="$PYTHONPATH:/path/to/models"
-```
-
-### Step 2: Get a model to test
-
-The provided script runs with the [Official version of ResNet trained with
-ImageNet data](/official/resnet), but can be used for other models as well,
-as long as you have a SavedModel or a Frozen Graph.
-
-You can download the ResNetv2-ImageNet [SavedModel](http://download.tensorflow.org/models/official/resnetv2_imagenet_savedmodel.tar.gz)
-or [Frozen Graph](http://download.tensorflow.org/models/official/resnetv2_imagenet_frozen_graph.pb),
-or, if you want to train the model yourself,
-pass `--export_dir` to the Official ResNet [imagenet_main.py](/official/resnet/imagenet_main.py).
-
-When running this script, you can pass in a SavedModel directory containing the
-Protobuf MetaGraphDef and variables directory to `savedmodel_dir`, or pass in
-a Protobuf frozen graph file directly to `frozen_graph`. If you downloaded the
-SavedModel linked above, note that you should untar it before passing in to the
-script.
-
-### Step 3: Get an image to test
-
-The script can accept a JPEG image file to use for predictions. If none is
-provided, random data will be generated. We provide a sample `image.jpg` here
-which can be passed in with the `--image_file` flag.
-
-### Step 4: Run the model
-
-You have TensorFlow, TensorRT, a graph def, and a picture.
-Now it's time to time.
-
-For the full set of possible parameters, you can run
-`python tensorrt.py --help`. Assuming you used the files provided above,
-you would run:
-
-```
-python tensorrt.py --frozen_graph=resnetv2_imagenet_frozen_graph.pb \
-  --image_file=image.jpg --native --fp32 --fp16 --int8 --output_dir=/my/output
-```
-
-This will print the predictions for each of the precision modes that were run
-(native, which is the native precision of the model passed in, as well
-as the TensorRT version of the graph at precisions of fp32, fp16 and int8):
-
-```
-INFO:tensorflow:Starting timing.
-INFO:tensorflow:Timing loop done!
-Predictions:
-Precision:  native [u'seashore, coast, seacoast, sea-coast', u'promontory, headland, head, foreland', u'breakwater, groin, groyne, mole, bulwark, seawall, jetty', u'lakeside, lakeshore', u'grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus']
-Precision:  FP32 [u'seashore, coast, seacoast, sea-coast', u'promontory, headland, head, foreland', u'breakwater, groin, groyne, mole, bulwark, seawall, jetty', u'lakeside, lakeshore', u'sandbar, sand bar']
-Precision:  FP16 [u'seashore, coast, seacoast, sea-coast', u'promontory, headland, head, foreland', u'breakwater, groin, groyne, mole, bulwark, seawall, jetty', u'lakeside, lakeshore', u'sandbar, sand bar']
-Precision:  INT8 [u'seashore, coast, seacoast, sea-coast', u'promontory, headland, head, foreland', u'breakwater, groin, groyne, mole, bulwark, seawall, jetty', u'grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus', u'lakeside, lakeshore']
-```
-
-The script will generate or append to a file in the output_dir, `log.txt`,
-which includes the timing information for each of the models:
-
-```
-==========================
-network: native_resnetv2_imagenet_frozen_graph.pb,	 batchsize 128, steps 3
-  fps 	median: 930.2, 	mean: 934.9, 	uncertainty: 5.9, 	jitter: 3.3
-  latency 	median: 0.13760, 	mean: 0.13692, 	99th_p: 0.13793, 	99th_uncertainty: 0.00271
-
-==========================
-network: tftrt_fp32_resnetv2_imagenet_frozen_graph.pb,	 batchsize 128, steps 3
-  fps 	median: 1160.2, 	mean: 1171.8, 	uncertainty: 18.0, 	jitter: 17.8
-  latency 	median: 0.11033, 	mean: 0.10928, 	99th_p: 0.11146, 	99th_uncertainty: 0.00110
-
-==========================
-network: tftrt_fp16_resnetv2_imagenet_frozen_graph.pb,	 batchsize 128, steps 3
-  fps 	median: 2007.2, 	mean: 2007.4, 	uncertainty: 0.4, 	jitter: 0.7
-  latency 	median: 0.06377, 	mean: 0.06376, 	99th_p: 0.06378, 	99th_uncertainty: 0.00001
-
-==========================
-network: tftrt_int8_resnetv2_imagenet_frozen_graph.pb,	 batchsize 128, steps 3
-  fps 	median: 2970.2, 	mean: 2971.4, 	uncertainty: 109.8, 	jitter: 279.4
-  latency 	median: 0.04309, 	mean: 0.04320, 	99th_p: 0.04595, 	99th_uncertainty: 0.00286
-```
-
-The script will also output the GraphDefs used for each of the modes run,
-for future use and inspection:
-
-```
-ls /my/output
-log.txt
-tftrt_fp16_resnetv2_imagenet_frozen_graph.pb
-tftrt_fp32_resnetv2_imagenet_frozen_graph.pb
-tftrt_int8_calib_resnetv2_imagenet_frozen_graph.pb
-tftrt_int8_resnetv2_imagenet_frozen_graph.pb
-```
-
-## Troubleshooting and Notes
-
-### GPU/Precision Compatibility
-
-Not all GPUs support the ops required for all precisions. For example, P100s
-cannot currently run INT8 precision.
-
-### Label Offsets
-
-Some ResNet models represent 1000 categories, and some represent all 1001, with
-the 0th category being "background". The models provided are of the latter type.
-If you are using a different model and find that your predictions seem slightly
-off, try passing in the `--ids_are_one_indexed` arg, which adjusts the label
-alignment for models with only 1000 categories.
-
-
-## Model Links
-[ResNet-v2-ImageNet Frozen Graph](http://download.tensorflow.org/models/official/resnetv2_imagenet_frozen_graph.pb)
-
-[ResNet-v2-ImageNet SavedModel](http://download.tensorflow.org/models/official/resnetv2_imagenet_savedmodel.tar.gz)
-
-[ResNet-v1-ImageNet Frozen Graph](http://download.tensorflow.org/models/official/resnetv1_imagenet_frozen_graph.pb)
-
-[ResNet-v1-ImageNet SavedModel](http://download.tensorflow.org/models/official/resnetv1_imagenet_savedmodel.tar.gz)
+- [TF-TRT repository](https://github.com/tensorflow/tensorrt) for conversion examples of various models.
+- [NVIDIA TF-TRT User Guide](https://docs.nvidia.com/deeplearning/frameworks/tf-trt-user-guide/index.html)